D3rlpy: An Offline Deep Reinforcement Learning Library
Abstract
In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL) library for Python. d3rlpy supports a set of offline deep RL algorithms as well as off-policy online algorithms via a fully documented plug-and-play API. To address a reproducibility issue, we conduct a large-scale benchmark with D4RL and Atari 2600 dataset to ensure implementation quality and provide experimental scripts and full tables of results. The d3rlpy source code can be found on GitHub: https://github.com/takuseno/d3rlpy.
Cite
Text
Seno and Imai. "D3rlpy: An Offline Deep Reinforcement Learning Library." Machine Learning Open Source Software, 2022.Markdown
[Seno and Imai. "D3rlpy: An Offline Deep Reinforcement Learning Library." Machine Learning Open Source Software, 2022.](https://mlanthology.org/mloss/2022/seno2022jmlr-d3rlpy/)BibTeX
@article{seno2022jmlr-d3rlpy,
title = {{D3rlpy: An Offline Deep Reinforcement Learning Library}},
author = {Seno, Takuma and Imai, Michita},
journal = {Machine Learning Open Source Software},
year = {2022},
pages = {1-20},
volume = {23},
url = {https://mlanthology.org/mloss/2022/seno2022jmlr-d3rlpy/}
}